• Gene regulation, chromatin, and epigenetics
• Cancer epigenomics
• Cell state and fate in development
• Single-cell heterogeneity

• R/Bioconductor tools for large-scale bioinformatic analysis
• Integrating large genome-scale datasets using high performance computing
• Applied machine learning
• Scientific computing, reproducibility, open data, and data sharing in genomics

The group studies how DNA encodes regulatory networks that enable cellular differentiation, and how these systems break down in disease. We ask fundamental questions about gene regulation, such as how regulatory DNA interacts to drive cellular programs, or how cells develop and respond to stimuli through chromatin remodeling at the single-cell level.

• Smith et al. (2021). PEPPRO: quality control and processing of nascent RNA profiling data
  Genome Biology. DOI: 10.1186/s13059-021-02349-4
• Smith et al. (2020). PEPATAC: An optimized ATAC-seq pipeline with serial alignments
  bioRxiv. DOI: 10.1101/2020.10.21.347054
• Lawson et al. (2020). COCOA: coordinate covariation analysis of epigenetic heterogeneity
  Genome Biology. DOI: 10.1186/s13059-020-02139-4
• Smith and Sheffield (2020). Analytical Approaches for ATAC-seq Data Analysis
  Current Protocols in Human Genetics. DOI: 10.1002/cphg.101
• Lawson et al. (2018). MIRA: An R package for DNA methylation-based inference of regulatory activity
  Bioinformatics. DOI: 10.1093/bioinformatics/bty083
• Wang et al. (2018). BART: a transcription factor prediction tool with query gene sets or epigenomic profiles
  Bioinformatics. DOI: 10.1093/bioinformatics/bty194
• Nagraj et al. (2018). LOLAweb: a containerized web server for interactive genomic locus overlap enrichment analysis
  Nucleic Acids Research. DOI: 10.1093/nar/gky464
• Sheffield et al. (2017). DNA methylation heterogeneity defines a disease spectrum in Ewing sarcoma
  Nature Medicine. DOI: 10.1038/nm.4273
• Sheffield and Bock (2016). LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor
  Bioinformatics. DOI: 10.1093/bioinformatics/btv612
• Klughammer et al. (2015). Differential DNA Methylation Analysis without a Reference Genome
  Cell Reports. DOI: 10.1016/j.celrep.2015.11.024
• Schmidl et al. (2015). ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors
  Nat. Methods. DOI: 10.1038/nmeth.3542
• Tomazou et al. (2015). Epigenome mapping reveals distinct modes of gene regulation and widespread enhancer reprogramming by the oncogenic fusion protein EWS-FLI1
  Cell Reports. DOI: 10.1016/j.celrep.2015.01.042
• Sheffield et al. (2013). Patterns of regulatory activity across diverse human cell-types predict tissue identity, transcription factor binding, and long-range interactions
  Genome Res. DOI: 10.1101/gr.152140.112
• Tewari et al. (2012). Chromatin accessibility reveals insights into androgen receptor activation and transcriptional specificity
  Genome Biol. DOI: 10.1186/gb-2012-13-10-r88
• ENCODE Consortium (2012). An integrated encyclopedia of DNA elements in the human genome
  Nature. DOI: 10.1038/nature11247
• Natarajan et al. (2012). Predicting cell-type-specific gene expression from regions of open chromatin
  Genome Res. DOI: 10.1101/gr.135129.111
• Thurman et al. (2012). The accessible chromatin landscape of the human genome
  Nature. DOI: 10.1038/nature11232
• Shibata et al. (2012). Extensive Evolutionary Changes in Regulatory Element Activity during Human Origins Are Associated with Altered Gene Expression and Positive Selection
  PLos Genet. DOI: 10.1371/journal.pgen.1002789
• Sheffield and Furey (2012). Identifying and characterizing regulatory sequences in the human genome with chromatin accessibility assays
  Genes. DOI: 10.3390/genes3040651
• Song et al. (2011). Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity
  Genome Res. DOI: 10.1101/gr.121541.111

Driven by biological interests in epigenomics and gene regulation, we analyze DNA methylation and chromatin accessibility and how these signals characterize cancers. Cancer is caused by a regulatory process run amok, and we study these regulatory programs in their normal and diseased state.

• Lawson et al. (2020). COCOA: coordinate covariation analysis of epigenetic heterogeneity
  Genome Biology. DOI: 10.1186/s13059-020-02139-4
• Corces et al. (2018). The chromatin accessibility landscape of primary human cancers
  Science. DOI: 10.1126/science.aav1898
• Klughammer et al. (2018). The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space
  Nature Medicine. DOI: 10.1038/s41591-018-0156-x
• Sheffield et al. (2017). DNA methylation heterogeneity defines a disease spectrum in Ewing sarcoma
  Nature Medicine. DOI: 10.1038/nm.4273
• Kovar et al. (2016). The second European interdisciplinary Ewing sarcoma research summit – A joint effort to deconstructing the multiple layers of a complex disease
  Oncotarget. DOI: 10.18632/oncotarget.6937
• Tomazou et al. (2015). Epigenome mapping reveals distinct modes of gene regulation and widespread enhancer reprogramming by the oncogenic fusion protein EWS-FLI1
  Cell Reports. DOI: 10.1016/j.celrep.2015.01.042

Using new microfluidics and sequencing technology technology, we are interested in asking fundamental questions about how cells differentiate and respond to their environments at the single cell level.

• Litzenburger et al. (2017). Single-cell epigenomic variability reveals functional cancer heterogeneity
  Genome Biol. DOI: 10.1186/s13059-016-1133-7
• Bock et al. (2016). Multi-Omics of Single Cells: Strategies and Applications
  Trends Biotechnol. DOI: 10.1016/j.tibtech.2016.04.004
• Schmidl et al. (2015). ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors
  Nat. Methods. DOI: 10.1038/nmeth.3542
• Farlik et al. (2015). Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics
  Cell Reports. DOI: 10.1016/j.celrep.2015.02.001

We are interested in research infrastructure to enable broader scientific computing in genomics and beyond, particularly the interoperability of data and analysis. The group is developing core infrastructure to solve general problems in scientific computing. As genomic and multi-omic data have increased in size, we develop novel models of genomic data and to build state-of-the-art APIs and systems that help biologists get the most out of data.

• Sheffield et al. (2021). Linking big biomedical datasets to modular analysis with Portable Encapsulated Projects
  bioRxiv. DOI: 10.1101/2020.10.08.331322
• Feng and Sheffield (2020). IGD: high-performance search for large-scale genomic interval datasets
  Bioinformatics. DOI: 10.1093/bioinformatics/btaa1062
• Stolarczyk et al. (2020). Refgenie: a reference genome resource manager
  GigaScience. DOI: 10.1093/gigascience/giz149
• Sheffield (2019). Bulker: a multi-container environment manager
  OSF Preprints. DOI: 10.31219/osf.io/natsj
• Feng et al. (2019). Augmented Interval List: a novel data structure for efficient genomic interval search
  Bioinformatics. DOI: 10.1093/bioinformatics/btz407
• Lawson et al. (2018). MIRA: An R package for DNA methylation-based inference of regulatory activity
  Bioinformatics. DOI: 10.1093/bioinformatics/bty083
• Sheffield et al. (2018). simpleCache: R caching for reproducible, distributed, large-scale projects
  The Journal of Open Source Software. DOI: 10.21105/joss.00463
• Sheffield and Bock (2016). LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor
  Bioinformatics. DOI: 10.1093/bioinformatics/btv612